In carrying out estimations for service projects (including physical projects such as manufacturing, construction, electronics, software development or services development), there is a need for creating a baseline (or multiple baselines). This baseline typically includes a set of historical projects and is used for future project prediction or estimation. Consequently, successful estimation relies on the accurate selection of proper candidate projects forming such a baseline set. One of the key characteristics of such a set of projects is that they come from a same or similar stochastic sample space with the same development and project execution environments. Furthermore, each sample project has to have reliable data quality.
Many projects face problems in development due to a lack of accurate estimation. Existing approaches commonly use a top-down approach to estimation using trend lines developed from a set of historical projects. However, these trend lines typically have been pre-determined from data that possess intrinsic uncertainty. Accordingly, existing approaches face challenges in that an estimation resulting from such techniques has a high degree of uncertainty.
Such uncertainty in project estimates can lead to potential business problems such as: 1) an overbid scenario where the estimate is too high and ultimately leads to the bidding enterprise losing a potential opportunity, and 2) an underbid scenario where the estimate is too low; the bid is won, but costs to complete the project cause a failure to meet gross profit targets, leading to a financially troubled project. Accordingly, a need exists for techniques to properly and accurate align a target project with an estimation model.